AI evidence extraction

The uses of generative artificial intelligence in social research north and south: Comparing empirical evidence from surveys in Denmark and Mexico

Markus S. Schulz, Ligia Tavera Fenollosa, Mauricio I. Dussauge Laguna, Oscar Fontanelli, Danay Quintana Nedelcu, Jackeline Alba Udave et al. · Methodological Innovations · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1177/20597991261454555

Methodology & findings

Study design

Comparative cross-national surveys conducted in Denmark and Mexico, examining faculty and student perceptions and usage patterns of generative artificial intelligence in research, teaching, and learning contexts..

Main result

The comparison reveals that "distinct patterns of usage by country, academic status, and gender" emerged from the data. Additionally, "Normative assessments among respondents vary across different research phases and tasks in which gen-AI is deployed," and "Open survey items suggest uncertainty about gen-AI's ethical and methodological suitability and a contrast between faculty emphasis on regulation and student prioritization of training."

Reports effect sizes.

Research paradigm

Empirical-pragmatist (mixed methods survey-based approach comparing cross-national contexts)

Author conclusions

The authors conclude that "the comparison of the two data sets aims to provide a foundation for reflection and discussion about normative frameworks, facilitative initiatives, and collective interventions at this historical juncture of enormous methodological innovation."

Risk of bias

Selection bias: Institutional sampling may not represent all higher education settings; Country-specific institutional differences (Denmark vs. Mexico) may confound comparisons; Potential self-selection bias in survey participation; Temporal bias: Rapid pace of gen-AI development may quickly date findings; Selection bias: Survey participants self-selected, potentially introducing response bias favoring those with stronger opinions about gen-AI; Geographic limitation: Only two countries (Denmark and Mexico) represent Global North and South perspectives; Institutional sampling: Limited to faculty and students at specific institutions, may not be representative of broader academic populations; Temporal bias: Snapshot of rapidly evolving technology landscape; findings may become outdated quickly; Selection bias: Surveys conducted only in two countries (Denmark and Mexico) with potentially different institutional accessibility and participation rates; Response bias: Self-reported usage and perceptions may not reflect actual practices; Geographic/institutional bias: Sampling limited to 'institutions within these two strongly contrasting countries' without specification of sampling strategy

Open questions raised

The authors identify a critical gap: "Lack of empirical data made it difficult to comprehend the situation and how to respond to it." They emphasize the necessity to "obtain empirical data to understand the extent and the ways in which these innovations are being used in research, teaching, and learning."
The authors identify that "Lack of empirical data made it difficult to comprehend the situation and how to respond to it," and note the necessity to "obtain empirical data to understand the extent and the ways in which these innovations are being used in research, teaching, and learning."
The authors identify the need for empirical data on gen-AI usage and perception, noting "Lack of empirical data made it difficult to comprehend the situation and how to respond to it." They call for understanding "the extent and the ways in which these innovations are being used in research, teaching, and learning" and suggest future work on developing "normative frameworks, facilitative initiatives, and collective interventions."

Data: not_statedCode: not_statedExtracted from: pdfAgreement 69%

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